mirror of https://github.com/ArduPilot/ardupilot
122 lines
5.4 KiB
Python
122 lines
5.4 KiB
Python
from __future__ import print_function
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from LogAnalyzer import Test,TestResult
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import DataflashLog
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# import scipy
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# import pylab #### TEMP!!! only for dev
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# from scipy import signal
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class TestDualGyroDrift(Test):
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'''test for gyro drift between dual IMU data'''
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def __init__(self):
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Test.__init__(self)
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self.name = "Gyro Drift"
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self.enable = False
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def run(self, logdata, verbose):
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self.result = TestResult()
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self.result.status = TestResult.StatusType.GOOD
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# if "IMU" not in logdata.channels or "IMU2" not in logdata.channels:
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# self.result.status = TestResult.StatusType.NA
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# return
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# imuX = logdata.channels["IMU"]["GyrX"].listData
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# imu2X = logdata.channels["IMU2"]["GyrX"].listData
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# # NOTE: weird thing about Holger's log is that the counts of IMU+IMU2 are different
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# print("length 1: %.2f, length 2: %.2f" % (len(imuX),len(imu2X)))
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# #assert(len(imuX) == len(imu2X))
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# # divide the curve into segments and get the average of each segment
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# # we will get the diff between those averages, rather than a per-sample diff as the IMU+IMU2 arrays are often not the same length
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# diffThresholdWARN = 0.03
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# diffThresholdFAIL = 0.05
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# nSamples = 10
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# imu1XAverages, imu1YAverages, imu1ZAverages, imu2XAverages, imu2YAverages, imu2ZAverages = ([],[],[],[],[],[])
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# imuXDiffAverages, imuYDiffAverages, imuZDiffAverages = ([],[],[])
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# maxDiffX, maxDiffY, maxDiffZ = (0,0,0)
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# sliceLength1 = len(logdata.channels["IMU"]["GyrX"].dictData.values()) / nSamples
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# sliceLength2 = len(logdata.channels["IMU2"]["GyrX"].dictData.values()) / nSamples
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# for i in range(0,nSamples):
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# imu1XAverages.append(numpy.mean(logdata.channels["IMU"]["GyrX"].dictData.values()[i*sliceLength1:i*sliceLength1+sliceLength1]))
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# imu1YAverages.append(numpy.mean(logdata.channels["IMU"]["GyrY"].dictData.values()[i*sliceLength1:i*sliceLength1+sliceLength1]))
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# imu1ZAverages.append(numpy.mean(logdata.channels["IMU"]["GyrZ"].dictData.values()[i*sliceLength1:i*sliceLength1+sliceLength1]))
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# imu2XAverages.append(numpy.mean(logdata.channels["IMU2"]["GyrX"].dictData.values()[i*sliceLength2:i*sliceLength2+sliceLength2]))
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# imu2YAverages.append(numpy.mean(logdata.channels["IMU2"]["GyrY"].dictData.values()[i*sliceLength2:i*sliceLength2+sliceLength2]))
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# imu2ZAverages.append(numpy.mean(logdata.channels["IMU2"]["GyrZ"].dictData.values()[i*sliceLength2:i*sliceLength2+sliceLength2]))
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# imuXDiffAverages.append(imu2XAverages[-1]-imu1XAverages[-1])
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# imuYDiffAverages.append(imu2YAverages[-1]-imu1YAverages[-1])
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# imuZDiffAverages.append(imu2ZAverages[-1]-imu1ZAverages[-1])
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# if abs(imuXDiffAverages[-1]) > maxDiffX:
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# maxDiffX = imuXDiffAverages[-1]
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# if abs(imuYDiffAverages[-1]) > maxDiffY:
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# maxDiffY = imuYDiffAverages[-1]
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# if abs(imuZDiffAverages[-1]) > maxDiffZ:
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# maxDiffZ = imuZDiffAverages[-1]
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# if max(maxDiffX,maxDiffY,maxDiffZ) > diffThresholdFAIL:
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# self.result.status = TestResult.StatusType.FAIL
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# self.result.statusMessage = "IMU/IMU2 gyro averages differ by more than %s radians" % diffThresholdFAIL
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# elif max(maxDiffX,maxDiffY,maxDiffZ) > diffThresholdWARN:
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# self.result.status = TestResult.StatusType.WARN
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# self.result.statusMessage = "IMU/IMU2 gyro averages differ by more than %s radians" % diffThresholdWARN
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# # pylab.plot(zip(*imuX)[0], zip(*imuX)[1], 'g')
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# # pylab.plot(zip(*imu2X)[0], zip(*imu2X)[1], 'r')
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# #pylab.plot(range(0,(nSamples*sliceLength1),sliceLength1), imu1ZAverages, 'b')
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# print("Gyro averages1X: " + repr(imu1XAverages))
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# print("Gyro averages1Y: " + repr(imu1YAverages))
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# print("Gyro averages1Z: " + repr(imu1ZAverages) + "\n")
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# print("Gyro averages2X: " + repr(imu2XAverages))
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# print("Gyro averages2Y: " + repr(imu2YAverages))
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# print("Gyro averages2Z: " + repr(imu2ZAverages) + "\n")
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# print("Gyro averages diff X: " + repr(imuXDiffAverages))
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# print("Gyro averages diff Y: " + repr(imuYDiffAverages))
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# print("Gyro averages diff Z: " + repr(imuZDiffAverages))
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# # lowpass filter using numpy
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# # cutoff = 100
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# # fs = 10000.0
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# # b,a = scipy.signal.filter_design.butter(5,cutoff/(fs/2))
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# # imuXFiltered = scipy.signal.filtfilt(b,a,zip(*imuX)[1])
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# # imu2XFiltered = scipy.signal.filtfilt(b,a,zip(*imu2X)[1])
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# #pylab.plot(imuXFiltered, 'r')
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# # TMP: DISPLAY BEFORE+AFTER plots
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# pylab.show()
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# # print("imuX average before lowpass filter: %.8f" % logdata.channels["IMU"]["GyrX"].avg())
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# # print("imuX average after lowpass filter: %.8f" % numpy.mean(imuXFiltered))
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# # print("imu2X average before lowpass filter: %.8f" % logdata.channels["IMU2"]["GyrX"].avg())
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# # print("imu2X average after lowpass filter: %.8f" % numpy.mean(imu2XFiltered))
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# avg1X = logdata.channels["IMU"]["GyrX"].avg()
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# avg1Y = logdata.channels["IMU"]["GyrY"].avg()
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# avg1Z = logdata.channels["IMU"]["GyrZ"].avg()
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# avg2X = logdata.channels["IMU2"]["GyrX"].avg()
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# avg2Y = logdata.channels["IMU2"]["GyrY"].avg()
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# avg2Z = logdata.channels["IMU2"]["GyrZ"].avg()
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# avgRatioX = (max(avg1X,avg2X) - min(avg1X,avg2X)) / #abs(max(avg1X,avg2X) / min(avg1X,avg2X))
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# avgRatioY = abs(max(avg1Y,avg2Y) / min(avg1Y,avg2Y))
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# avgRatioZ = abs(max(avg1Z,avg2Z) / min(avg1Z,avg2Z))
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# self.result.statusMessage = "IMU gyro avg: %.4f,%.4f,%.4f\nIMU2 gyro avg: %.4f,%.4f,%.4f\nAvg ratio: %.4f,%.4f,%.4f" % (avg1X,avg1Y,avg1Z, avg2X,avg2Y,avg2Z, avgRatioX,avgRatioY,avgRatioZ)
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